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#!/scratch/Anaconda2.4.0/bin/python
import pandas as pd
import numpy as np
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
import os
import time
"""
This Module is structured to work with a raw data at the following JSON format:
{'setup_name': {'test1_name':[QUERY1,QUERY2,QUERY3],
'test2_name':[QUERY1,QUERY2,QUERY3]
}
'setup_name2': {'test1_name':[QUERY1,QUERY2,QUERY3],
'test2_name':[QUERY1,QUERY2,QUERY3]
}
}
The Query structure is set (currently) to this:
(test_name,state, date,hour,minute,mpps_result,mpps_min,mpps_max,build_id) example:
["syn attack - 64 bytes, single CPU", "stl", "20161226", "01", "39", "9.631898", "9.5", "11.5", "54289"]
it can be changed to support other formats of queries, simply change the enum class to support your desired structure
the enums specify the indexes of the data within the query tuple
"""
class TestQuery(object):
QUERY_DATE = 2 # date format is yyyymmdd
QUERY_HOUR = 3
QUERY_MINUTE = 4
QUERY_MPPS_RESULT = 5
QUERY_TEST_MIN = 6
QUERY_TEST_MAX = 7
QUERY_BUILD_ID = 8
class Test:
def __init__(self, name, setup_name, end_date):
self.name = name
self.setup_name = setup_name
self.end_date = end_date
self.stats = [] # tuple
self.results_df = [] # dataFrame
self.latest_result = [] # float
self.latest_result_date = '' # string
def analyze_all_test_data(self, raw_test_data):
test_results = []
test_dates = []
test_build_ids = []
test_mins = set()
test_maxs = set()
for query in raw_test_data:
date_formatted = time.strftime("%d-%m-%Y", time.strptime(query[int(TestQuery.QUERY_DATE)], "%Y%m%d"))
time_of_res = date_formatted + '-' + query[int(TestQuery.QUERY_HOUR)] + ':' + query[
int(TestQuery.QUERY_MINUTE)]
test_dates.append(time_of_res)
test_results.append(float(query[int(TestQuery.QUERY_MPPS_RESULT)]))
test_build_ids.append(query[int(TestQuery.QUERY_BUILD_ID)])
test_mins.add(float(query[int(TestQuery.QUERY_TEST_MIN)]))
test_maxs.add(float(query[int(TestQuery.QUERY_TEST_MAX)]))
test_results_df = pd.DataFrame({self.name: test_results, self.name + ' Date': test_dates,
"Setup": ([self.setup_name] * len(test_results)), "Build Id": test_build_ids},
dtype='str')
stats = tuple(
[float(test_results_df[self.name].mean()), min(test_mins), max(test_maxs)]) # stats = (avg_mpps,min,max)
self.latest_result = float(test_results_df[self.name].iloc[-1])
self.latest_result_date = str(test_results_df[test_results_df.columns[3]].iloc[-1])
self.results_df = test_results_df
self.stats = stats
class Setup:
def __init__(self, name, start_date, end_date, raw_setup_data):
self.name = name
self.start_date = start_date # string of date
self.end_date = end_date # string of date
self.tests = [] # list of test objects
self.all_tests_data_table = pd.DataFrame() # dataframe
self.setup_trend_stats = pd.DataFrame() # dataframe
self.latest_test_results = pd.DataFrame() # dataframe
self.raw_setup_data = raw_setup_data # dictionary
self.test_names = raw_setup_data.keys() # list of names
def analyze_all_tests(self):
for test_name in self.test_names:
t = Test(test_name, self.name, self.end_date)
t.analyze_all_test_data(self.raw_setup_data[test_name])
self.tests.append(t)
def analyze_latest_test_results(self):
test_names = []
test_dates = []
test_latest_results = []
for test in self.tests:
test_names.append(test.name)
test_dates.append(test.latest_result_date)
test_latest_results.append(test.latest_result)
self.latest_test_results = pd.DataFrame(
{'Date': test_dates, 'Test Name': test_names, 'MPPS\Core (Norm)': test_latest_results},
index=range(1, len(test_latest_results) + 1))
self.latest_test_results = self.latest_test_results[[2, 1, 0]] # re-order columns to name|MPPS|date
def analyze_all_tests_stats(self):
test_names = []
all_test_stats = []
for test in self.tests:
test_names.append(test.name)
all_test_stats.append(test.stats)
self.setup_trend_stats = pd.DataFrame(all_test_stats, index=test_names,
columns=['Avg MPPS/Core (Norm)', 'Golden Min', 'Golden Max'])
self.setup_trend_stats.index.name = 'Test Name'
def analyze_all_tests_trend(self):
all_tests_trend_data = []
for test in self.tests:
all_tests_trend_data.append(test.results_df)
self.all_tests_data_table = reduce(lambda x, y: pd.merge(x, y, how='outer'), all_tests_trend_data)
def plot_trend_graph_all_tests(self, save_path='', file_name='_trend_graph.png'):
for test_name in self.test_names:
self.all_tests_data_table[test_name].plot(style=['.-'])
plt.legend(fontsize='small', loc='best')
plt.ylabel('MPPS/Core (Norm)')
plt.title('Setup: ' + self.name)
plt.tick_params(
axis='x',
which='both',
bottom='off',
top='off',
labelbottom='off')
plt.xlabel('Time Period: ' + self.start_date + ' - ' + self.end_date)
if save_path:
plt.savefig(os.path.join(save_path, self.name + file_name))
if not self.setup_trend_stats.empty:
(self.setup_trend_stats.round(2)).to_csv(os.path.join(save_path, self.name +
'_trend_stats.csv'))
plt.close('all')
def plot_latest_test_results_bar_chart(self, save_path='', img_file_name='_latest_test_runs.png',
stats_file_name='_latest_test_runs_stats.csv'):
plt.figure()
colors_for_bars = ['b', 'g', 'r', 'c', 'm', 'y']
self.latest_test_results[[1]].plot(kind='bar', legend=False,
color=colors_for_bars) # plot only mpps data, which is in column 1
plt.xticks(rotation='horizontal')
plt.xlabel('Index of Tests')
plt.ylabel('MPPS/Core (Norm)')
plt.title("Test Runs for Setup: " + self.name)
if save_path:
plt.savefig(os.path.join(save_path, self.name + img_file_name))
(self.latest_test_results.round(2)).to_csv(
os.path.join(save_path, self.name + stats_file_name))
plt.close('all')
def analyze_all_setup_data(self):
self.analyze_all_tests()
self.analyze_latest_test_results()
self.analyze_all_tests_stats()
self.analyze_all_tests_trend()
def plot_all(self, save_path=''):
self.plot_latest_test_results_bar_chart(save_path)
self.plot_trend_graph_all_tests(save_path)
def latest_runs_comparison_bar_chart(setup_name1, setup_name2, setup1_latest_result, setup2_latest_result,
save_path=''
):
s1_res = setup1_latest_result[[0, 1]] # column0 is test name, column1 is MPPS\Core
s2_res = setup2_latest_result[[0, 1, 2]] # column0 is test name, column1 is MPPS\Core, column2 is Date
s1_res.columns = ['Test Name', setup_name1]
s2_res.columns = ['Test Name', setup_name2, 'Date']
compare_dframe = pd.merge(s1_res, s2_res, on='Test Name')
compare_dframe.plot(kind='bar')
plt.legend(fontsize='small', loc='best')
plt.xticks(rotation='horizontal')
plt.xlabel('Index of Tests')
plt.ylabel('MPPS/Core (Norm)')
plt.title("Comparison between " + setup_name1 + " and " + setup_name2)
if save_path:
plt.savefig(os.path.join(save_path, "_comparison.png"))
compare_dframe = compare_dframe.round(2)
compare_dframe.to_csv(os.path.join(save_path, '_comparison_stats_table.csv'))
# WARNING: if the file _all_stats.csv already exists, this script deletes it, to prevent overflowing of data
def create_all_data(ga_data, start_date, end_date, save_path='', detailed_test_stats=''):
all_setups = {}
all_setups_data = []
setup_names = ga_data.keys()
for setup_name in setup_names:
s = Setup(setup_name, start_date, end_date, ga_data[setup_name])
s.analyze_all_setup_data()
s.plot_all(save_path)
all_setups_data.append(s.all_tests_data_table)
all_setups[setup_name] = s
if detailed_test_stats:
if os.path.exists(os.path.join(save_path, '_detailed_table.csv')):
os.remove(os.path.join(save_path, '_detailed_table.csv'))
all_setups_data_dframe = pd.DataFrame().append(all_setups_data)
all_setups_data_dframe.to_csv(os.path.join(save_path, '_detailed_table.csv'))
trex07setup = all_setups['trex07']
trex08setup = all_setups['trex08']
latest_runs_comparison_bar_chart('Mellanox ConnectX-4',
'Intel XL710', trex07setup.latest_test_results,
trex08setup.latest_test_results,
save_path=save_path)
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